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Interactive Jupyter widgets to visualize images, point sets, and meshes in 2D and 3D

Project description

PyPI version npm Build status Interactive example on MyBinder License Software citation DOI

Interactive Jupyter widgets to visualize images, point sets, and meshes on the web.

itkwidgets chest CT in JupyterLab

Key Features:

itkwidgets demo

These widgets are designed to support spatial analysis with the Insight Toolkit (ITK), but they work equally well with other spatial analysis tools in the scientific Python ecosystem.

These widgets are built on itk.js and vtk.js.


To install the widgets for the Jupyter Notebook with pip:

pip install itkwidgets

For Jupyter Lab, additionally, run:

jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter-matplotlib jupyterlab-datawidgets itkwidgets


JupyterLab 3 support is not yet available. JupyterLab 2 or the Jupyter Notebook are possible alternatives.


In Jupyter, import the view function:

from itkwidgets import view

Then, call the view function at the end of a cell, passing in the image to examine:


For information on additional options, see the view function docstring:


Other available widgets:

  • itkwidgets.line_profile: Plot an intensity line profile.
  • itkwidgets.checkerboard: Compare two images in a checkerboard pattern.
  • Compare two images side-by-side.

Using within a Docker Container

You can use itkwidgets from within a docker container with jupyterlab. To create a local docker image:

Install docker and build the docker image with:

git clone
cd itkwidgets/docker
docker build -t $IMAGE .

Then run the docker container with:

docker run -it --rm -v $EXAMPLESDIR:/home/jovyan -p 8888:8888 itkwidgets:0.1.0

Finally, connect to your notebook at

Advanced Usage

The itkwidgets are based on ipywidgets. As a consequence, widgets traits can be queried, assigned, or observed with the viewer object returned by the view function. itkwidgets can be combined with other ipywidgets to quickly explore algorithm parameters, create graphical interfaces, or create data visualization dashboards.

Mouse Controls

Left click + drag
Right click + drag or shift + left click + drag
Mouse wheel or control + left click + drag or pinch
Alt + left click + drag left-right
Change color transfer function window
Shift + left click + drag top-bottom
Change color transfer function level
Shift + alt + left click + drag top-bottom
Change primary Gaussian volume opacity transfer function magnitude

Keyboard Shortcuts

Keyboard shortcuts take effect when the mouse is positioned inside the viewer. All shortcuts are prefixed with Alt+. Corresponding keys for the Dvorak keyboard layout have the same effect.

Alt + 1
X-plane mode
Alt + 2
Y-plane mode
Alt + 3
Z-plane mode
Alt + 4
Volume rendering mode
Alt + q
Toggle user interface
Alt + w
Toggle region of interest (ROI) selection widget
Alt + e
Reset ROI
Alt + r
Reset camera
Alt + s
Toggle slicing planes in volume rendering mode
Alt + f
Toggle fullscreen


IOPub data rate exceeded.

If you experience the notebook warning:

IOPub data rate exceeded.
The notebook server will temporarily stop sending output
to the client in order to avoid crashing it.
To change this limit, set the config variable

Set the notebook configuration value:

jupyter notebook --NotebookApp.iopub_data_rate_limit=1e12

Scrolling in JupyterLab

Cell output scrolls by default in JupyterLab. To disable scrolling, right click in the region to the left of the output and select Disable Scrolling for Outputs.

‘Permission denied’ during installation

If Permission denied errors occur during installation, install the Python package with user permission via:

pip install –user itkwidgets

For JupyterLab extension installation, configure JupyterLab to use your user application directory by setting the environmental variable, JUPYTERLAB_DIR:

export JUPYTERLAB_DIR=$HOME/.local/share/jupyter/lab

Check that this is picked up in the value of the Application directory reported by:

jupyter lab path

Then, install the extension as usual:

jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter-matplotlib jupyterlab-datawidgets itkwidgets


Participation is welcome! For a development installation (requires Node.js):

git clone
cd itkwidgets
python -m pip install -r requirements-dev.txt -r requirements.txt
python -m pip install -e .
jupyter nbextension install --py --symlink --sys-prefix itkwidgets
jupyter nbextension enable --py --sys-prefix itkwidgets
jupyter nbextension enable --py --sys-prefix widgetsnbextension
python -m pytest
python -m pytest --nbmake examples/*.ipynb

The above commands will setup your system for development with the Jupyter Notebook. In one terminal, start Jupyter:

cd itkwidgets
jupyter notebook

In another terminal, put Webpack in watch mode to rebuild any Javascript changes when you save a Javascript file:

cd itkwidgets
npm run watch

If Python code is changed, restart the kernel to see the changes. If Javascript code is changed, reload the page after to Webpack has finished building.

To develop for Jupyter Lab, additionally run:

jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter-matplotlib jupyterlab-datawidgets jupyter-webrtc
jupyter labextension install ./js
jupyter lab --watch


Historical note: this project was previously named itk-jupyter-widgets, but it was renamed to itkwidgets to be consistent with the package name.


This project is under active development. Its API and behavior may change at any time. We mean it.

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